Learning scikit-learn: Machine Learning in Python

Learning

Raúl Garreta, Guillermo MoncecchiNovember 2013

Incorporating machine learning in your applications is becoming essential. As a programmer this book is the ideal introduction to scikit-learn for your Python environment, taking your skills to a whole new level.

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Book Details

ISBN 139781783281930

Paperback118 pages

About This Book

Use Python and scikit-learn to create intelligent applications

Apply regression techniques to predict future behaviour and learn to cluster items in groups by their similarities

Make use of classification techniques to perform image recognition and document classification

Who This Book Is For

If you are a programmer who wants to explore machine learning and data-based methods to build intelligent applications and enhance your programming skills, this the book for you. No previous experience with machine-learning algorithms is required.

Table of Contents

Chapter 1: Machine Learning – A Gentle Introduction

Installing scikit-learn

Our first machine learning method –linear classification

Evaluating our results

Machine learning categories

Important concepts related to machine learning

Summary

Chapter 2: Supervised Learning

Image recognition with Support Vector Machines

Text classification with Naïve Bayes

Explaining Titanic hypothesis with decision trees

Predicting house prices with regression

Summary

Chapter 3: Unsupervised Learning

Principal Component Analysis

Clustering handwritten digits with k-means

Alternative clustering methods

Summary

Chapter 4: Advanced Features

Feature extraction

Feature selection

Model selection

Grid search

Parallel grid search

Summary

What You Will Learn

Set up scikit-learn inside your Python environment

Classify objects (from documents to human faces and flower species) based on some of their features, using a variety of methods from Support Vector Machines to Naïve Bayes

Use Decision Trees to explain the main causes of certain phenomenon such as the Titanic passengers’ survival

Predict house prices using regression techniques

Display and analyse groups in your data using dimensionality reduction

Make use of different tools to preprocess, extract, and select the learning features

Select the best parameters for your models using model selection

Improve the way you build your models using parallelization techniques

In Detail

Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of “big data”, huge amounts of information is being generated. This makes machine learning an unavoidable source of new data-based approximations for problem solving.

With Learning scikit-learn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. The book combines an introduction to some of the main concepts and methods in machine learning with practical, hands-on examples of real-world problems. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikit-learn and Python.

The book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using real-world applications and advanced features, it takes a deep dive into the various machine learning techniques.

You will learn to evaluate your results and apply advanced techniques for preprocessing data. You will also be able to select the best set of features and the best methods for each problem.

With Learning scikit-learn: Machine Learning in Python you will learn how to use the Python programming language and the scikit-learn library to build applications that learn from experience, applying the main concepts and techniques of machine learning.

Authors

Raúl Garreta

Raúl Garreta is a Computer Engineer with much experience in the theory and application of Artificial Intelligence (AI), where he specialized in Machine Learning and Natural Language Processing (NLP).
He has an entrepreneur profile with much interest in the application of science, technology, and innovation to the Internet industry and startups. He has worked in many software companies, handling everything from video games to implantable medical devices.
In 2009, he co-founded Tryolabs with the objective to apply AI to the development of intelligent software products, where he performs as the CTO and Product Manager of the company. Besides the application of Machine Learning and NLP, Tryolabs' expertise lies in the Python programming language and has been catering to many clients in Silicon Valley. Raul has also worked in the development of the Python community in Uruguay, co-organizing local PyDay and PyCon conferences.

Guillermo Moncecchi

Guillermo Moncecchi is a Natural Language Processing researcher at the Universidad de la República of Uruguay. He received a PhD in Informatics from the Universidad de la República, Uruguay and a Ph.D in Language Sciences from the Université Paris Ouest, France. He has participated in several international projects on NLP. He has almost 15 years of teaching experience on Automata Theory, Natural Language Processing, and Machine Learning.
He also works as Head Developer at the Montevideo Council and has lead the development of several public services for the council, particularly in the Geographical Information Systems area. He is one of the Montevideo Open Data movement leaders, promoting the publication and exploitation of the city's data.

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